Neighborhood Disadvantage and Prostate Tumor RNA Expression of Stress-Related Genes

Key Points Question Is neighborhood disadvantage associated with prostate tumor RNA expression of stress-related genes among African American and White men with prostate cancer? Findings In this cross-sectional study of 218 men with prostate cancer, neighborhood disadvantage was associated with significantly higher tumor expression of stress-related genes, including several proinflammatory genes. The association between the Area Deprivation Index and HTR6 (serotonin pathway) remained statistically significant after multiple-comparison adjustment. Meaning These findings suggest that neighborhood disadvantage is associated with higher expression of stress-related genes, which may contribute to an increased risk of aggressive prostate cancer and warrants follow-up in future studies.


eMethods. Risk Stratification
We categorized the participants in our sample into prostate cancer risk stratification categories according to guidelines set by the American Urological Association (AUA) 3 .Specifically, we used information on grade group, tumor stage, and pre-operative prostate-specific antigen (PSA) to classify participants as low-risk, intermediate-risk, or high-risk.We used pathologic Gleason score to determine the grade group and pathologic tumor stage to determine the tumor stage since the study participants comprised men who received radical prostatectomy surgery for prostate cancer.In the case that participants satisfied two of the three criteria for low-risk prostate cancer according to AUA guidelines (PSA < 10 ng/mL, grade group 1, and stage 1-2A) but had a missing value for the other criterion, we considered such participants to have low-risk prostate cancer.

RNA expression data
We leveraged available transcriptomic data from formalin-fixed, paraffin-embedded (FFPE) prostate tumor tissue from our previous studies.The pathology and RNA extraction approaches have been described previously 4 .Briefly, pathology review identified the most representative tumor sample from the FFPE blocks.Based on an H&E slide, we circled ~2 mm tumor regions (>=75% purity), then cut two unstained double-thickness slides (10 µm) per sample, with the same circled areas marked for RNA extraction.RNA was extracted using Qiagen's RNEasy FFPE kit.We obtained RNA expression data from the Human Clariom D™ array, which measures expression for 138,745 transcript cluster (TC) IDs.We analyzed a subset of 105 TCs corresponding to stress-related genes, including those in the Conserved Transcriptional Response to Adversity (CTRA) 5 and stress-related signaling pathways, encompassing the adrenergic, glucocorticoid, dopaminergic, serotoninergic, and muscarinic systems 6 (eTable 1 ).Various stress-related signaling genes have been previously linked with lethal prostate cancer 6 and there is relevance of CTRA genes (e.g., proinflammatory genes) to prostate cancer risk and progression [7][8][9] .Similar to our previous work 4 , we performed SST-RMA normalization and log base-2 transformation of the RNA expression data with TAC software, version 4.0.

Neighborhood variables
We included several measures of neighborhood disadvantage in the present study, considering the census tract containing each participant's address at diagnosis to constitute the neighborhood.The first such measure was the Area Deprivation Index (ADI) 10,11 , which includes 17 neighborhood-level variables in the income, education, employment and housing quality domains and employs 5-year estimates from the American Community Survey (ACS) beginning in 2005.This index uses a set of fixed importance weights for component variables that were estimated in a previous factor analysis 11 that do not consider associations with the present outcome.The ADI has been used in several previous analyses of neighborhood deprivation [12][13][14] and is standardized to have a mean score of 100 and standard deviation of 20, with greater values indicating greater deprivation.We used the R package "sociome" to obtain ADI scores.We calculated the ADI measure for 160 participants (117 African American and 43 White participants).
Our second measure was a custom neighborhood deprivation index (NDI) that we fit with the validated Bayesian index model developed by our group 15 .The construction of the NDI was similar to that of the ADI, but importance weights for components in the index were estimated in modeling, allowing identification of the most important variables in the index.We have found in previous studies that the Bayesian index model is able to accurately and consistently estimate index effects in addition to the relative importance of component variables in the index 15 and that an NDI can provide better model fit than other measures that assume equal importance for all components in the index 16 .We included 16 census tract-level variables in the NDI that we have used in previous work 17,18 : median household income, median house value, percent of structures built in 1939 or earlier, median rent as a percentage of household income, percent renters, percent in poverty, percent receiving public assistance/SNAP benefits, percent vacant structures, percent Black, percent Hispanic, percent without high school diploma, percent unemployed, percent single-parent households with children under 18, percent with no vehicle, percent lacking complete plumbing facilities, and percent crowded households.While many variables included in the ADI and NDI captured common themes (i.e., house value, income, education, and children), there were a few differences; the ADI included percent in white-collar occupations and income disparity, and the NDI included more racial and housing  characteristics.We inverted the household income and house value variables in the NDI so that all variables would be in the direction of greater deprivation and converted these variables to all be in 2020 inflation-adjusted U.S. dollars.We calculated the NDI measure for 211 participants (162 African American and 49 White participants).
The third measure we used was the spatial Racial Isolation (RI) index 19 , which was available for 2009 and later.This measure ranges from 0 to 1 for a given racial group and considers the racial composition of census geographies as well as that of their neighbors.Following previous research, we calculated the racial isolation of African American residents, conceptualizing increasing values of this measure to proxy increasing concentrations of social and economic disadvantage, in response to the continuing history of anti-Black racism in the United States 20- 23 .We calculated the RI measure for 136 participants (99 African American and 37 White participants).
We considered historical redlining for our final neighborhood measure given the associations identified in the literature between living in a formerly redlined neighborhood and negative health outcomes.The process of redlining occurred when the Home Owners' Loan Corporation (HOLC) assigned "grades" to neighborhoods in urban areas.These grades were a proxy of HOLC's perceived risk for real estate investment in these areas, and were often a function of neighborhood racial, ethnic, and/or religious characteristics 24 .Redlining influenced the likelihood of HOLC providing mortgages, as well as refinancing (to avoid foreclosure on existing mortgages), for individuals in the neighborhood.Living in historically redlined neighborhoods has been associated with a variety of negative health outcomes in the present day 25 .We used the HOLC redlining map of Baltimore from the 1930s, which overlaid neighborhood grades of A, B, C, and D on George Cram's 1935 "Street Map of Baltimore Area" that also included Towson, Pikesville, and Catonsville.Certain neighborhoods, such as those outside of the city center, did not receive a grade from HOLC.For this measure we considered the exposure to be binary, taking on a value of 1 if the residence fell within a neighborhood graded D (the worst grade) on the HOLC map, and 0 otherwise (A, B, C, or no grade).We used the digitized redlining map produced by the Mapping Inequality project (University of Richmond) 26 .We calculated the redlining measure for 218 participants (168 African American and 50 White participants).

Statistical analysis
We fit linear regression models for each gene in relation to each neighborhood measure, adjusting for race (African American and White), age at surgery (<=49, 50-59, 60-69, and 70+ years), and year of surgery (1992-1997, 1998-2004, 2005-2010, 2011-2016, and 2017-2021, with groupings defined based on our study distribution).For the ADI, RI, and historical redlining exposures, we fit frequentist linear regression models with the log base-2 transformed gene expression values as the response.We reported coefficients, standard errors, and p-values, as well as q-values (p-values adjusted for the 105 genes tested with each exposure with the Benjamini-Hochberg method 27 ), using a conventional .05level of significance.For the NDI, we fit Bayesian linear regression models, reporting coefficients, credible intervals, and exceedance probabilities (EPs), considering EPs of 90% or greater to constitute significance.We used the R package "rjags" for model fitting 28 .In the Bayesian models we assigned vague Normal priors to each regression coefficient, β  ∼ (0, τ  ), τ  = 1/σ 2 , σ  ∼ (0,10).Additionally, we assigned a Dirichlet prior with vector of parameters  for the index weights in the NDI so that each weight would be between 0 and 1 and the weights would sum to 1.We also calculated the Spearman correlations between the continuous neighborhood measures to evaluate their agreement.As a sensitivity analysis for the associations between the neighborhood measures and the stress-related genes, we repeated the models among African American men only to assess whether the significant findings were similar to the primary analysis among African American and White men combined, adjusting for age and year of surgery.We also assessed characteristics of the larger group of African American and White men receiving radical prostatectomy for prostate cancer at the University of Maryland Medical Center from 1992-2021 to assess the extent to which our sample reflected this larger group.We performed an additional sensitivity analysis that compared socioeconomic and healthcare access indicators 1,2 of geographic areas in Maryland (census tracts, counties) contributing to our sample vs. those that did not contribute to explore the representativeness of our sample with respect to Maryland as a whole.Finally, we assessed whether primary payer at diagnosis, which would be associated with individual-level socioeconomic status, was a confounder of our top associations (those with p<.05 in our primary analysis) in another sensitivity analysis.We performed all analyses in R, version 4.3.1 29 ; tests were two-tailed.

eFigure 2 .
Area Deprivation Index Values of Participants in Sample Higher values indicate greater neighborhood deprivation.Participant locations are slightly jittered to maintain confidentiality.eFigure 3. Racial Isolation Index Values of Participants in Sample Higher values indicate greater African American residential segregation.Participant locations are slightly jittered to maintain confidentiality.
Summary of Comparing Socioeconomic and Health Care Access Indicators Between Counties and Census Tracts in Maryland Contributing vs Not Contributing Participants to our Sample of Men Who Received Radical Prostatectomy Surgery for Prostate Cancer at the University of Maryland Medical Center, 1992-2021 County-level data derived from United States Department of Agriculture Economic Research Service datasets, 2017-2021 1 .Access to healthcare defined by minimum distance to closest National Cancer Institute designated cancer center 2 .Socioeconomic indicators tested with two-sample unequal variance t-tests and healthcare access tested with chi-squared tests of independence.eTable 3. Summary of Genes Associated With the Neighborhood Measures in Adjusted Models for African American Men Who Received Radical Prostatectomy Surgery for Prostate Cancer at the University of Maryland Medical Center, 1992-2021 : ADI, Area Deprivation Index; EP, exceedance probability; NDI, Neighborhood Deprivation Index; RI, Racial Isolation Index; se, standard error; TC; transcript cluster ID; 2.5 th P and 97.5 th P, boundaries of 95% credible interval for the beta coefficient Summary of Sensitivity Analysis Additionally Adjusting for Primary Payer at Diagnosis for Genes Associated With the Neighborhood Measures in Adjusted Models Among African American and White Men Who Received Radical Prostatectomy Surgery for Prostate Cancer at the University of Maryland Medical Center, 1992-2021 p-value adjusted for multiple comparisons using the Benjamini-Hochberg False Discovery Rate approach c Percent change (absolute) in beta when conducting sensitivity analysis that additionally adjusted models for primary payer at diagnosis (private insurance, Medicaid, Medicare, Military/Veterans Affairs, unspecified insurance, uninsured) d exceedance probability (probability of being greater than zero) eFigure 1. Flowchart Illustrating Sample Selection *denotes oversampling of African American men.
Abbreviations: CTRA, Conserved Transcriptional Response to Adversity; IFN, interferon; TC, transcript cluster ID ‡IFI30 is the gene that is listed in the CTRA ^CXCL8 is synonymous with IL8; IFIT1B is synonymous with IFIT1L, and IGLL3P is synonymous with IGLL3 in the CTRA eTable 2.AbbreviationsaFrom linear regression modeling the given gene in relation to the neighborhood measure, adjusted for age at surgery and year of surgery b p-value adjusted for multiple comparisons using the Benjamini-Hochberg False Discovery Rate approach c exceedance probability (probability of being greater than zero) eTable 4.Abbreviations: ADI, Area Deprivation Index; EP, exceedance probability; IFN, interferon; NDI, Neighborhood Deprivation Index; RI, Racial Isolation Index; se, standard error; TC; transcript cluster ID; 2.5 th P and 97.5 th P, boundaries of 95% credible interval for the beta coefficient a From linear regression modeling the given gene in relation to the neighborhood measure, adjusted for race, age at surgery, year of surgery, and primary payer at diagnosis b